Social network link prediction aims to analyze existing network information to infer future links. This is a highly challenging task because social networks exhibit complex spatio-temporal coupling, characterized by dynamic evolution over time (i.e., temporal dependency) and interdependent correlations across space (i.e., spatial dependency). Existing methods mainly focus on time-domain and space-domain modeling and do not fully utilize the frequency-domain information of graph snapshot sequence data. This study proposes a time-frequency-aware graph convolutional network (TFAGCN) for social network link prediction. The main advantage of TFAGCN is that frequency-, time-, and space-domain information is integrated together in a unified network framework, which captures both global temporal dependencies and local spatio-temporal dependencies in social network data. Specifically, this study introduces a frequency-domain module and a spatio-temporal module. In the frequency-domain module, the fast Fourier transform is used to transform the graph snapshot sequence, and the multilayer perceptron is used to learn the real and imaginary parts of the frequency components in the frequency domain to capture the global dependencies of social networks. In the spatio-temporal module, a graph convolutional neural network and a gated recurrent unit network are jointly used to explore the local topological features and local temporal features of individual graph snapshots, respectively. In addition, a time-frequency bidirectional cross-attention network is designed to adaptively fuse frequency-domain features with time-domain features. To address link prediction for large-scale social networks, a simplified version of TFAGCN, namely TFAGS, is designed. Extensive experiments conducted on six real-world social network datasets show that the proposed method outperforms state-of-the-art baseline methods in link prediction, thus confirming its effectiveness as an efficient new tool for social network information representation. The code of TFAGCN is available in this repository: https://github.com/junlonglai/TFAGCN.